Role : Sr Snowflake Data Architect
Edinburgh, UK(Hybrid-2days onsite)
Permanent & Contract
Key Responsibilities
Define the enterprise data strategy, reference architectures, and target-state
blueprints anchored on the Snowflake AI Data Cloud, spanning data
ingestion, storage, processing, serving, and governance.
Lead end-to-end modernization and migration programs to Snowflake —
including legacy EDW estates (e.g., Teradata, SQL Server), data
lakes/lakehouses, and integration platforms — covering migration
assessment, roadmap, cutover, parallel-run validation, and decommission
plans.
Govern data modeling standards and patterns (dimensional star/snowflake,
Data Vault, 3NF, wide-table designs), semantic layers (Snowflake semantic
views, Horizon Context, BI semantic models), and data contracts for analytical
and operational domains.
Establish ELT/ETL and streaming architectures on Snowflake — Snowpipe,
Snowpipe Streaming, Dynamic Tables, Streams and Tasks, external tables,
and Apache Iceberg tables — along with orchestration standards (dbt, Airflow,
Snowflake Tasks) and CI/CD/DataOps best practices.
Implement security-by-design in Snowflake: RBAC, tag-based governance
(ABAC), dynamic data masking, row/column-level policies, Tri-Secret/external
key management, network policies, Private Link, SSO/OAuth/MFA, audit
trails, and access history.
Embed data quality, observability, and lineage using Snowflake Horizon
Catalog and native observability features; define SLAs/SLOs, reconciliation
frameworks, and incident/RCAs for data reliability.
Architect cross-region and cross-cloud data sharing, replication, and failover
strategies (BC/DR) leveraging secure shares, Snowflake
Marketplace/exchange, clean rooms, zero-copy cloning, Time Travel, and
Fail-safe.
Drive Snowflake cost optimization and FinOps practices — virtual warehouse
sizing and scaling policies, auto-suspend/resume, resource monitors, and
query performance tuning (clustering keys, Search Optimization, materialized
views, result caching, adaptive compute).
Enable AI- and GenAI-ready architectures on Snowflake — Cortex AI, agentic
governance, and semantic layers for AI agents — ensuring a governed, AI-
ready data foundation.
Partner with business, product, analytics, and engineering leaders to prioritize
roadmaps and translate requirements into scalable Snowflake architectures.
Produce architecture artifacts (logical/physical models, ADRs, data flow
diagrams, runbooks) and ensure governance alignment with enterprise
standards.
Mentor and lead engineers and data modelers; conduct design and code
reviews; elevate engineering practices across teams.
Required Skills & Experience
20+ years in data architecture and engineering, including 8-10+ years in
lead/enterprise architect roles delivering large-scale data platforms, with at
least 5+ years of hands-on Snowflake architecture experience.
Proven track record architecting and delivering enterprise data warehousing
and lakehouse solutions on Snowflake, including migrations from legacy EDW
(Teradata, Oracle, SQL Server, etc.) and modern platforms (BigQuery,
Redshift, Synapse, etc.).
Deep data modeling expertise (dimensional, Data Vault, 3NF) and semantic
design, including Snowflake semantic views and Horizon Context.
Strong ELT/ETL and orchestration background — Snowflake-native pipelines
(Snowpipe, Dynamic Tables, Streams, Tasks), dbt (including dbt Projects on
Snowflake), Airflow, Azure Data Factory, Informatica, or similar.
Hands-on experience with major cloud platforms (AWS, Azure, or GCP) —
including Snowflake deployment, networking (Private Link), and cloud storage
integration (S3, ADLS, GCS); familiarity with lakehouse stacks (Databricks,
Delta Lake, Apache Iceberg) is a plus.
Mastery of Snowflake security and governance: RBAC, tag-based
governance, dynamic data masking, row access policies, column-level
security, Tri-Secret encryption, network policies, Horizon Catalog, access
history, and data classification.
Streaming and CDC patterns (Kafka, Kinesis, Snowpipe Streaming) and
event-driven architectures for near real-time use cases.
Data governance and metadata management (catalogs, lineage, MDM, data
quality frameworks) with familiarity in regulatory regimes (e.g.,
GDPR/CCPA/HIPAA/PCI-DSS/SOX).
Proficiency in SQL and at least one programming language for data
engineering (Python preferred), plus CI/CD and IaC (Git, pipelines,
Terraform/CloudFormation) — including declarative, version-controlled
Snowflake object management.
Proven ability to assess legacy estates, create pragmatic Snowflake migration
roadmaps, and deliver phased outcomes with measurable business value.
Excellent communication and stakeholder management; ability to influence
architecture decisions and align cross-functional teams.
Snowflake certifications (SnowPro Core, SnowPro Advanced: Architect) are
strongly preferred
